188,447 research outputs found
A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning
Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.</p
Adaptive visual sampling
PhDVarious visual tasks may be analysed in the context of sampling from the visual field. In visual
psychophysics, human visual sampling strategies have often been shown at a high-level to
be driven by various information and resource related factors such as the limited capacity of
the human cognitive system, the quality of information gathered, its relevance in context and
the associated efficiency of recovering it. At a lower-level, we interpret many computer vision
tasks to be rooted in similar notions of contextually-relevant, dynamic sampling strategies
which are geared towards the filtering of pixel samples to perform reliable object association. In
the context of object tracking, the reliability of such endeavours is fundamentally rooted in the
continuing relevance of object models used for such filtering, a requirement complicated by realworld
conditions such as dynamic lighting that inconveniently and frequently cause their rapid
obsolescence. In the context of recognition, performance can be hindered by the lack of learned
context-dependent strategies that satisfactorily filter out samples that are irrelevant or blunt the
potency of models used for discrimination. In this thesis we interpret the problems of visual
tracking and recognition in terms of dynamic spatial and featural sampling strategies and, in this
vein, present three frameworks that build on previous methods to provide a more flexible and
effective approach.
Firstly, we propose an adaptive spatial sampling strategy framework to maintain statistical object
models for real-time robust tracking under changing lighting conditions. We employ colour
features in experiments to demonstrate its effectiveness. The framework consists of five parts:
(a) Gaussian mixture models for semi-parametric modelling of the colour distributions of multicolour
objects; (b) a constructive algorithm that uses cross-validation for automatically determining
the number of components for a Gaussian mixture given a sample set of object colours; (c) a
sampling strategy for performing fast tracking using colour models; (d) a Bayesian formulation
enabling models of object and the environment to be employed together in filtering samples by
discrimination; and (e) a selectively-adaptive mechanism to enable colour models to cope with
changing conditions and permit more robust tracking.
Secondly, we extend the concept to an adaptive spatial and featural sampling strategy to deal
with very difficult conditions such as small target objects in cluttered environments undergoing
severe lighting fluctuations and extreme occlusions. This builds on previous work on dynamic
feature selection during tracking by reducing redundancy in features selected at each stage as
well as more naturally balancing short-term and long-term evidence, the latter to facilitate model
rigidity under sharp, temporary changes such as occlusion whilst permitting model flexibility
under slower, long-term changes such as varying lighting conditions. This framework consists of
two parts: (a) Attribute-based Feature Ranking (AFR) which combines two attribute measures;
discriminability and independence to other features; and (b) Multiple Selectively-adaptive Feature
Models (MSFM) which involves maintaining a dynamic feature reference of target object
appearance. We call this framework Adaptive Multi-feature Association (AMA). Finally, we present an adaptive spatial and featural sampling strategy that extends established
Local Binary Pattern (LBP) methods and overcomes many severe limitations of the traditional
approach such as limited spatial support, restricted sample sets and ad hoc joint and disjoint statistical
distributions that may fail to capture important structure. Our framework enables more
compact, descriptive LBP type models to be constructed which may be employed in conjunction
with many existing LBP techniques to improve their performance without modification. The
framework consists of two parts: (a) a new LBP-type model known as Multiscale Selected Local
Binary Features (MSLBF); and (b) a novel binary feature selection algorithm called Binary Histogram
Intersection Minimisation (BHIM) which is shown to be more powerful than established
methods used for binary feature selection such as Conditional Mutual Information Maximisation
(CMIM) and AdaBoost
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Tracking
With efficient appearance learning models, Discriminative Correlation Filter
(DCF) has been proven to be very successful in recent video object tracking
benchmarks and competitions. However, the existing DCF paradigm suffers from
two major issues, i.e., spatial boundary effect and temporal filter
degradation. To mitigate these challenges, we propose a new DCF-based tracking
method. The key innovations of the proposed method include adaptive spatial
feature selection and temporal consistent constraints, with which the new
tracker enables joint spatial-temporal filter learning in a lower dimensional
discriminative manifold. More specifically, we apply structured spatial
sparsity constraints to multi-channel filers. Consequently, the process of
learning spatial filters can be approximated by the lasso regularisation. To
encourage temporal consistency, the filter model is restricted to lie around
its historical value and updated locally to preserve the global structure in
the manifold. Last, a unified optimisation framework is proposed to jointly
select temporal consistency preserving spatial features and learn
discriminative filters with the augmented Lagrangian method. Qualitative and
quantitative evaluations have been conducted on a number of well-known
benchmarking datasets such as OTB2013, OTB50, OTB100, Temple-Colour, UAV123 and
VOT2018. The experimental results demonstrate the superiority of the proposed
method over the state-of-the-art approaches
Online Feature Selection for Visual Tracking
Object tracking is one of the most important tasks in many applications of computer vision. Many tracking methods use a fixed set of features ignoring that appearance of a target object may change drastically due to intrinsic and extrinsic factors. The ability to dynamically identify discriminative features would help in handling the appearance variability by improving tracking performance. The contribution of this work is threefold. Firstly, this paper presents a collection of several modern feature selection approaches selected among filter, embedded, and wrapper methods. Secondly, we provide extensive tests regarding the classification task intended to explore the strengths and weaknesses of the proposed methods with the goal to identify the right candidates for online tracking. Finally, we show how feature selection mechanisms can be successfully employed for ranking the features used by a tracking system, maintaining high frame rates. In particular, feature selection mounted on the Adaptive Color Tracking (ACT) system operates at over 110 FPS. This work demonstrates the importance of feature selection in online and realtime applications, resulted in what is clearly a very impressive performance, our solutions improve by 3% up to 7% the baseline ACT while providing superior results compared to 29 state-of-the-art tracking methods
Automated construction of a hierarchy of self-organized neural network classifiers
This paper documents an effort to design and implement a neural network-based, automatic classification system which dynamically constructs and trains a decision tree. The system is a combination of neural network and decision tree technology. The decision tree is constructed to partition a large classification problem into smaller problems. The neural network modules then solve these smaller problems. We used a variant of the Fuzzy ARTMAP neural network which can be trained much more quickly than traditional neural networks. The research extends the concept of self-organization from within the neural network to the overall structure of the dynamically constructed decision hierarchy. The primary advantage is avoidance of manual tedium and subjective bias in constructing decision hierarchies. Additionally, removing the need for manual construction of the hierarchy opens up a large class of potential classification applications. When tested on data from real-world images, the automatically generated hierarchies performed slightly better than an intuitive (handbuilt) hierarchy. Because the neural networks at the nodes of the decision hierarchy are solving smaller problems, generalization performance can really be improved if the number of features used to solve these problems is reduced. Algorithms for automatically selecting which features to use for each individual classification module were also implemented. We were able to achieve the same level of performance as in previous manual efforts, but in an efficient, automatic manner. The technology developed has great potential in a number of commercial areas, including data mining, pattern recognition, and intelligent interfaces for personal computer applications. Sample applications include: fraud detection, bankruptcy prediction, data mining agent, scalable object recognition system, email agent, resource librarian agent, and a decision aid agent
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